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Jon Saad-FalconandClaude Opus 4.6
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Sets up a complete documentation website with 7 navigable sections (Home, Getting Started, User Guide, Architecture, API Reference, Deployment, Development), light/dark mode, search, code copy, and Mermaid diagram support. API reference pages use mkdocstrings to auto-generate docs from source docstrings. GitHub Actions workflow deploys to GitHub Pages on push to main. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
185 lines
4.9 KiB
Markdown
185 lines
4.9 KiB
Markdown
---
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title: OpenJarvis
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description: Programming abstractions for on-device AI
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---
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# OpenJarvis
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**Programming abstractions for on-device AI.**
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OpenJarvis is a modular framework for building, running, and learning from local AI systems. It provides composable abstractions across four core pillars — Intelligence, Engine, Agentic Logic, and Memory — with a cross-cutting trace-driven learning system that improves routing decisions over time.
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Everything runs on your hardware. Cloud APIs are optional.
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---
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## Key Features
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<div class="grid cards" markdown>
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- **Four Core Pillars**
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---
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Intelligence (model routing), Engine (inference runtime), Agentic Logic (tool-calling agents), and Memory (persistent searchable storage) — each with a clear ABC interface and decorator-based registry.
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- **5 Engine Backends**
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---
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Ollama, vLLM, SGLang, llama.cpp, and cloud (OpenAI/Anthropic/Google). All implement the same `InferenceEngine` ABC with `generate()`, `stream()`, `list_models()`, and `health()`.
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- **5 Memory Backends**
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---
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SQLite/FTS5 (default, zero-dependency), FAISS, ColBERTv2, BM25, and Hybrid (reciprocal rank fusion). Document chunking, indexing, and context injection built in.
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- **Hardware-Aware**
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---
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Auto-detects GPU vendor, model, and VRAM via `nvidia-smi`, `rocm-smi`, and `system_profiler`. Recommends the optimal engine for your hardware automatically.
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- **Offline-First**
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---
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All core functionality works without a network connection. Cloud API backends are optional extras for when you need them.
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- **OpenAI-Compatible API**
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---
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`jarvis serve` starts a FastAPI server with `POST /v1/chat/completions`, `GET /v1/models`, and SSE streaming. Drop-in replacement for OpenAI-compatible clients.
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- **Trace-Driven Learning**
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---
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Every agent interaction is recorded as a trace. The learning system uses accumulated traces to improve model routing decisions. Pluggable router policies: heuristic, trace-driven, and GRPO.
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- **Python SDK**
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---
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The `Jarvis` class provides a high-level sync API. Three lines of code to ask a question. Full access to agents, tools, memory, and model routing.
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- **CLI-First**
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---
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`jarvis ask`, `jarvis serve`, `jarvis memory`, `jarvis bench`, `jarvis telemetry` — every capability is accessible from the command line with rich terminal output.
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</div>
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---
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## Quick Start
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### Python SDK
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```python
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from openjarvis import Jarvis
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j = Jarvis()
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response = j.ask("Explain quicksort in two sentences.")
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print(response)
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j.close()
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```
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For more control, use `ask_full()` to get usage stats, model info, and tool results:
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```python
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result = j.ask_full(
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"What is 2 + 2?",
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agent="orchestrator",
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tools=["calculator"],
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)
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print(result["content"]) # "4"
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print(result["tool_results"]) # [{tool_name: "calculator", ...}]
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```
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### CLI
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```bash
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# Ask a question
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jarvis ask "What is the capital of France?"
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# Use an agent with tools
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jarvis ask --agent orchestrator --tools calculator,think "What is 137 * 42?"
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# Start the API server
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jarvis serve --port 8000
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# Index documents and search memory
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jarvis memory index ./docs/
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jarvis memory search "configuration options"
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# Run inference benchmarks
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jarvis bench run --json
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```
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---
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## Project Status
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OpenJarvis v1.0 is complete. The framework includes the full four-pillar architecture, Python SDK, CLI, OpenAI-compatible API server, OpenClaw agent infrastructure, benchmarking framework, and Docker deployment. The test suite contains over 1,000 tests. Phase 6 (trace system and trace-driven learning) is in active development.
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| Component | Status |
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|-----------|--------|
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| Intelligence (model routing) | Stable |
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| Engine (5 backends) | Stable |
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| Agentic Logic (agents + tools) | Stable |
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| Memory (5 backends) | Stable |
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| Python SDK | Stable |
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| CLI | Stable |
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| API Server | Stable |
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| Trace System | Active Development |
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| Trace-Driven Learning | Active Development |
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| Docker Deployment | Stable |
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---
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## Documentation
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<div class="grid cards" markdown>
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- **[Getting Started](getting-started/installation.md)**
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---
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Install OpenJarvis, configure your first engine, and run your first query in minutes.
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- **[User Guide](user-guide/cli.md)**
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---
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Comprehensive guides for the CLI, Python SDK, agents, memory, tools, telemetry, and benchmarks.
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- **[Architecture](architecture/overview.md)**
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---
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Deep dive into the four-pillar design, registry pattern, query flow, and cross-cutting learning system.
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- **[API Reference](api/index.md)**
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---
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Auto-generated reference for every module: SDK, core, engine, agents, memory, tools, intelligence, learning, traces, telemetry, and server.
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- **[Deployment](deployment/docker.md)**
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---
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Deploy OpenJarvis with Docker, systemd, or launchd. Includes GPU-accelerated container images.
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- **[Development](development/contributing.md)**
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---
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Contributing guide, extension patterns, roadmap, and changelog.
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</div>
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